forked from BoyuanJiang/Age-Gender-Estimate-TF
/
convert_to_records.py
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/
convert_to_records.py
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Converts MNIST data to TFRecords file format with Example protos."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os
import sys
from datetime import datetime
from scipy.io import loadmat
import tensorflow as tf
from imutils.face_utils import FaceAligner
from imutils.face_utils import rect_to_bb
import argparse
import imutils
import dlib
import cv2
import pandas as pd
import numpy as np
import skimage.io as io
from tqdm import tqdm
from sklearn.model_selection import train_test_split
FLAGS = None
def _int64_feature(value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
def _bytes_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
def convert_to(data_set, name):
"""Converts a dataset to tfrecords."""
file_name = data_set.file_name
genders = data_set.gender
ages = data_set.age
face_score = data_set.score
second_face_score = data_set.second_score
num_examples = data_set.shape[0]
base_dir = "data/imdb_crop"
# initialize dlib's face detector (HOG-based) and then create
# the facial landmark predictor and the face aligner
shape_predictor = 'shape_predictor_68_face_landmarks.dat'
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(shape_predictor)
fa = FaceAligner(predictor, desiredFaceWidth=64)
error=0
total=0
# if images.shape[0] != num_examples:
# raise ValueError('Images size %d does not match label size %d.' %
# (images.shape[0], num_examples))
# rows = images.shape[1]
# cols = images.shape[2]
# depth = images.shape[3]
filename = os.path.join(name + '.tfrecords')
print('Writing', filename)
with tf.python_io.TFRecordWriter(filename) as writer:
for index in tqdm(range(num_examples)):
if face_score[index] < 0.75:
continue
# if (~np.isnan(second_face_score[index])) and second_face_score[index] > 0.0:
# continue
if ~(0 <= ages[index] <= 100):
continue
if np.isnan(genders[index]):
continue
try:
# image_raw = io.imread(os.path.join(base_dir,file_names[index])).tostring()
# image_raw = open(os.path.join(base_dir,str(file_name[index][0]))).read()
# load the input image, resize it, and convert it to grayscale
image = cv2.imread(os.path.join(base_dir,str(file_name[index][0])),cv2.IMREAD_COLOR)
image = imutils.resize(image, width=256)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
rects = detector(gray, 2)
if len(rects)!=1:
continue
else:
image_raw = fa.align(image, gray, rects[0])
image_raw = image_raw.tostring()
except IOError: #some files seem not exist in face_data dir
error = error+1
pass
# image_raw = images[index].tostring()
example = tf.train.Example(features=tf.train.Features(feature={
# 'height': _int64_feature(rows),
# 'width': _int64_feature(cols),
# 'depth': _int64_feature(depth),
'age': _int64_feature(int(ages[index])),
'gender':_int64_feature(int(genders[index])),
'image_raw': _bytes_feature(image_raw)}))
writer.write(example.SerializeToString())
total = total+1
print("There are ",error," missing pictures" )
print("Found" ,total, "valid faces")
def get_meta(mat_path, db):
meta = loadmat(mat_path)
full_path = meta[db][0, 0]["full_path"][0]
dob = meta[db][0, 0]["dob"][0] # Matlab serial date number
gender = meta[db][0, 0]["gender"][0]
photo_taken = meta[db][0, 0]["photo_taken"][0] # year
face_score = meta[db][0, 0]["face_score"][0]
second_face_score = meta[db][0, 0]["second_face_score"][0]
age = [calc_age(photo_taken[i], dob[i]) for i in range(len(dob))]
data = {"file_name": full_path, "gender": gender, "age": age, "score": face_score,
"second_score": second_face_score}
dataset = pd.DataFrame(data)
return dataset
def calc_age(taken, dob):
birth = datetime.fromordinal(max(int(dob) - 366, 1))
# assume the photo was taken in the middle of the year
if birth.month < 7:
return taken - birth.year
else:
return taken - birth.year - 1
def main(unused_argv):
# Get the data.
# data_sets = pd.read_csv("gender_age_train.txt", header=None, sep=" ")
# data_sets.columns = ["file_name", "gender", "age"]
data_sets = get_meta('./data/imdb_crop/imdb.mat','imdb')
# data_sets = data_sets[data_sets.age >= 0]
# data_sets = data_sets[data_sets.age <= 100]
train_sets,test_sets = train_test_split(data_sets,train_size=0.001,random_state=2017)
train_sets.reset_index(drop=True, inplace=True)
test_sets.reset_index(drop=True, inplace=True)
# data_sets = mnist.read_data_sets(FLAGS.directory,
# dtype=tf.uint8,
# reshape=False,
# validation_size=FLAGS.validation_size)
# Convert to Examples and write the result to TFRecords.
convert_to(train_sets, 'train')
convert_to(test_sets,'test')
# convert_to(data_sets.validation, 'validation')
# convert_to(data_sets.test, 'test')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
# parser.add_argument(
# '--directory',
# type=str,
# default='/tmp/data',
# help='Directory to download data files and write the converted result'
# )
# parser.add_argument(
# '--validation_size',
# type=int,
# default=5000,
# help="""\
# Number of examples to separate from the training data for the validation
# set.\
# """
# )
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)